Wenny Rahayu

Wenny Rahayu


Engineering and Mathematical Sciences School

at La Trobe University, Victoria, Australia



Wenny Rahayu is currently the Head of Engineering and Mathematical Sciences School at La Trobe University. She is the Leader of Data Analysis and Information Systems research within the DRP (Disciplinary Research Program) flagship of Mathematical and Computing Science. In the last 10 years, she has done substantial work in the area of database integration and optimization, knowledge discovery, and big data management. She has been chief-investigators of three ARC (Australian Research Council) Linkage grants, Industry collaboration grant (Airservices and IPL Australia), international grants (Open Geospatial Consortium, Japan JSPS, and Australia Indonesia AIGRP), the Australian Army (Army Research), and the AAS (Australia Academy of Science).  She is currently on the editorial board of the International Journal of Web and Grid Services and the International Journal of Space-Based and Situated Computing.  She has been invited to give numerous talks and tutorials on The Global Information Exchange and Big Data Interoperability at a number of international conferences.  So far she has published around 200 papers with more than 3900 citations on the above topics.


Privacy Preservation in Big Databases


The advancement of scientific discoveries, management of social issues, and enterprise innovation depend a lot on the availability of large data collection (big data). The more sophisticated technology we have for data collection, the more important data we can collect, the more critical it is to be able to derive useful knowledge from this big data collection.  The main issue to be discussed in this talk is “how can big data collection be used for knowledge discovery without violating individual privacy and sensitive information”.

Sensitive information may include medical conditions, income, background, preferences, etc. These data collections are often critical for high-level decision making purposes, however it is important to ensure that individual privacy is maintained at all times. In this talk, the focus will be on the different privacy mechanisms in Open Data scenario, where data collection is made available for research, analysis, or marketing purposes.  A range of well-known techniques and algorithms in data generalisations and anonymization will be discussed, along side a new proposed dissection technique for semi-structured or tree-structured data set which is often found in standardised data such as medical information.

03 July 2017  9.30 – 10.30 ROOM OA (AULA MAGNA H3) BUILDING H3